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KTH ROYAL INSTITUTE OF TECHNOLOGY
Pawel Herman
Department of Computational Science and Technology,
School of Computer Science and Communication
KTH Royal Institute of Technology, Sweden
Introduction to Neuroinformatics Importance of Modelling and Simulations
Neuroscience course, May 17
Why is it important to study brains?
• Quest for knowledge
• Computational inspiration
• Brain disorders and diseases
http://www.modernmedicalguide.com/alzheimers-disease/
2
Marja-Leena Line, INCF
Where are we today?
• collecting more data, building more advanced models, performing more complex analyses
3
Where are we today?
• collecting more data, building more advanced models, performing more complex analyses • BUT:
– studying individual components of neural systems with little integration/generalisation effort – focused on a limited set of spatio-temporal scales – working on disconnected data sets using different tools, procedures, protocols – disappointing reproducibility of experimental work (much improved for simulations) – poor understanding of neural mechanisms (computational primitives) at any level of the organisation -> richer computational mindset needed
4
Where are we today?
What could we do to advance the brain science?
Progress is not satisfactory and the needs are immense, so are the implications of
the advancement of brain science
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Where are we today?
What could we do to advance the brain science?
Combine efforts, collaborate interdisciplinarily, organise and share data, integrate biological evidence, build multi-scale models etc.
Progress is not satisfactory and the needs are immense, so are the implications of
the advancement of brain science
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What is neuroinformatics?
Neuroinformatics is the mean to connect neuroscience, medical science, information technology and computer science.
NEUROSCIENCE
COMPUTER SCIENCEINFORMATION TECHNOLOGY (IT)
NEUROINFORMATICS
NEUROMEDICINE
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What is neuroinformatics?
Neuroinformatics is the mean to connect neuroscience, medical science, information technology and computer science.
NEUROSCIENCE
COMPUTER SCIENCEINFORMATION TECHNOLOGY (IT)
NEUROINFORMATICS
NEUROMEDICINE
NEUROSCIENCE eSCIENCE
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Fundamental goals of neuroinformatics
ICT-based brain research – aims and implications
• organise and integrate neuroscience data
• accelerate our quest for understanding the brain
• support neuromedicine, understanding brain diseases
• develop brain-inspired future computing technologies, brain-like intelligent systems
• promote education, deliver multi-disciplinary training.
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Fundamental goals of neuroinformatics
ICT-based brain research – fundamental goals
• organise and integrate neuroscience data
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Fundamental goals of neuroinformatics
ICT-based brain research – fundamental goals
• organise and integrate neuroscience data
“Data tsunami”
“We are drowning in information but starved for knowledge” John Naisbitt
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Data Age ̶ Multiomic Neuroscience Data
From sub-cellular resolution to whole brain resolution Sean Hill, INCF
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ICT-based brain research – fundamental goals
• organise and integrate multi-level data
Fundamental goals of neuroinformatics
gather existing data across scales and levels,
identify missing data
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Sean Hill, INCF
ICT-based brain research – fundamental goals
• organise and integrate multi-level data
Fundamental goals of neuroinformatics
gather existing data across scales and levels,
identify missing data
from genes to behaviour
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organise and integrate multi-level data
Focus for neuroinformatics – data, theory
gather existing data at multiple levels,
identify missing data
devise new approaches to data analysis
develop tools for storing, visualising
and sharing information
build databases, brain atlases
build models, simulate, develop theories
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organise and integrate multi-level data
Focus for neuroinformatics – data, theory
gather existing data at multiple levels,
identify missing data
devise new approaches to data analysis
develop tools for storing, visualising
and sharing information
build databases, brain atlases
build models, simulate, develop theories
13
Modelling – towards integrative neuroscience
What is the purpose of computational modelling?
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adapted by A Kumar
Modelling – towards integrative neuroscience
What is the purpose of computational modelling?
• to integrate available data and build theories
• to describe & understand the underlying mechanisms
• to reveal causal relationships
• to generate insights and predictions for experimental neuroscience, etc.
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adapted by A Kumar
Modelling in neuroscience
• What is a model?
Mathematical model is a description of a system using mathematical concepts - rules, mainly in terms of formulae, e.g.
)()( tIRtudtdu
m +−=τsubthreshold activity in the
integrate-and-fire (IF) model
14
An example of a single neuron model – HH formalism
• Various levels of mathematical description – • zooming in (on details) vs. zooming out (abstraction)
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An example of a single neuron model – rate unit
• Various levels of mathematical description – • zooming in (on details) vs. zooming out (abstraction)
y = φ(Σ wi xi) φ y
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An example of a single neuron model – rate unit
• Various levels of mathematical description – • zooming in (on details) vs. zooming out (abstraction)
y = φ(Σ wi xi) φ
time
y
time
y
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Modelling strategy – bridging levels
THEORY, GLOBAL / FUNCTIONAL PRINCIPLES
NEURAL IMPLEMENTATION
(DYNAMICS, ARCHITECTURE)
”top-down”
NEURAL DETAIL, WEALTH OF BIOLOGICAL DATA
EMERGING PHENOMENA, HIGHER-LEVEL FUNCTION /
DYNAMICS
”bottom-up” Gerstner et al., Science 2012
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Modelling strategy – bridging levels
THEORY, GLOBAL / FUNCTIONAL PRINCIPLES
NEURAL IMPLEMENTATION
(DYNAMICS, ARCHITECTURE)
”top-down”
Synthetic LFP
Fre
qu
ency
(H
z)
10
20
30
40
50
60
2 3 4 5 6 7 8Time (seconds)
NEURAL DETAIL, WEALTH OF BIOLOGICAL DATA
EMERGING PHENOMENA, HIGHER-LEVEL FUNCTION /
DYNAMICS
”bottom-up”
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Modelling strategy – bridging levels
THEORY, GLOBAL / FUNCTIONAL PRINCIPLES
NEURAL IMPLEMENTATION
(DYNAMICS, ARCHITECTURE)
”top-down”
NEURAL DETAIL, WEALTH OF BIOLOGICAL DATA
EMERGING PHENOMENA, HIGHER-LEVEL FUNCTION /
DYNAMICS
”bottom-up”
SUITABLE STRATEGY AND LEVEL OF DETAIL
RESEARCH QUESTION (DATA, CONSTRAINTS)
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Computational modelling approaches
How can we go about modelling?
Phenomenological models – mathematical description of phenomena without handling constituent parts
Mechanistic models – description of a system in terms of its constituent parts
Statistical models – description of a system in terms of random variables and their distributions (they can be mechanistic or phenomenological)
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David Marr’s theoretical approach
Computational mindset of David Marr
Three levels of description:
1. Computational level – what does the system do?
• What logic defines the nature of resulting mental representations of incoming stimuli?
2. Algorithmic level – how does the system do it?
• What processes are involved in building mental representations? How is input translate to output?
3. Implementation level – how is the system physically realised – implemented?
• What is the neural hardware – substrate?
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1945-1980
Typical modelling workflow 1. Defining the model: the research question and the research hypothesis
determine the type of model, the model components, and the approach to solving the model.
2. Parameter fitting: complex high-dimensional models (biophysical) often have a huge parameter space that cannot be fully explored, instead parameters are fitted from the data, available data influences construction of the model.
3. Simulation: model is implemented in the suitable simulator(s), the obtained simulation results are analyzed and visualized.
4. Validation: the model is confronted with more experimental data, the model behaviour should correspond to the modelled biological system (at least qualitatively).
5. Prediction: good models have predictive power, when additionally perturbed they can show the behaviour of the system under the new conditions.
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My general modelling philosophy
Develop or build on a theory
Model/simulate functional aspects
Implement neural substrate
abstract and conceptual functional models
detailed models with neural dynamics
translate constrain
cognitive phenomena, behavioural
effects
anatomy, signal
recordings
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My general modelling philosophy
Develop or build on a theory
Recurrent associative memory (Hopfield, 1982)
Cortical attractor networks
Model/simulate functional aspects
Implement neural substrate
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Cortical attractor memory model example
Local basket cell
Local pyramidal
Local RSNP
Distant pyramidal
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Cortical column
Hopfield network
Hypercolumn with columns
Cortical attractor
model
From abstract to biologically detailed implementation
Hopfield recurrent neural network
mapping to biology
• individual neurons
• neural populations
• cortical columns (Mountcastle et al., 1955)
(the concept of a cell assembly, Hebb’s association)
(horizontal connections in the cortical layer 2/3 implementing recurrency)
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Biologically detailed cortical models
MINICOLUMN
HYPERCOLUMN
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Modular structure with hypercolumns consisting of minicolumnar units (distributed patterns)
Cortical attractor memory model
20
Hopfield network
Cortical patch
Cortical attractor
model
mapping to biology
Cortical memory function
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Herman, Lundqvist et al. (2013) Brain Research Lundqvist, Herman et al. (2011) J Cogn Neurosci
0 1 2 3 4 5 6 7seconds
completion
bistability, competition
Oscillatory phenomena
Herman, Lundqvist et al. (2013) Brain Research Lundqvist, Herman et al. (2011) J Cogn Neurosci
22
Synthetic LFP
Freq
uenc
y (H
z)
10
20
30
40
50
60
2 3 4 5 6 7 8Time (seconds)
A holistic computational model of mammalian olfactory system
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receptors and olfactory receptor cells
Buck and Axel, 1991
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olfactory bulb
A holistic computational model of mammalian olfactory system
Benjaminsson, Herman et al.(2012) Buck and Axel, 1991
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Buck and Axel, 1991
olfactory cortex
A holistic computational model of mammalian olfactory system
Benjaminsson, Herman et al.(2012)
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A wide spectrum of results
Abstract model of mammalian olfaction
Benjaminsson, Herman et al.(2012)
How do computational models help?
• Integrate (and fit) experimental data
• Describe neural systems – provide mechanistic understanding of the neural system
• Make predictions about the system behavior
in new conditions
• Provide new ways to study brain diseases
• Provide principles to develop new technology (brain-like computing, neuromorphic systems, control for robots)
Marja-Leena
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Future challenges for computational neuroscience
• Design of biologically realistic models that span over many levels of spatial organization and a wide range of temporal scales
• The need for development of multi-scale interoperable simulation tools (e.g. MUSIC)
• Further advancement of simulation technology allowing for interactive control with visualisation capabilities
• Enforcing tighter links with biology – interactive and iterative process that deeply involves experimental work
48
Trends and future outlook – what do we need for that?
• more theory and simulations
• tighter connections with experimentalists (from genes to behaviour)
• computational power for large-scale massively parallel simulations
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Trends and future outlook – what do we need for that?
• more theory and simulations
• tighter connections with experimentalists (from genes to behaviour)
• computational power for large-scale massively parallel simulations
• tools for simulations, analysis and visualisation
MOOSE
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